{"id":"W1986871321","doi":"10.1145/362084.362142","title":"Knowledge discovery in data warehouses","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Online analytical processing; Data warehouse; Automatic summarization; Terabyte; Knowledge extraction; Data mining; Process (computing); Data science; Information retrieval; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01270963,0.00107387,0.002919577,0.0120867,0.002303649,0.01416133,0.004637019,0.002289707,0.002428933],"category_scores_gemma":[0.03968966,0.001935199,0.003017743,0.01926387,0.002862549,0.01757523,0.006502985,0.003447293,0.001854885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002119804,"about_ca_system_score_gemma":0.002914435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003744675,"about_ca_topic_score_gemma":0.00376621,"domain_scores_codex":[0.9862226,0.004681997,0.001820122,0.001995355,0.004780157,0.0004997748],"domain_scores_gemma":[0.9754484,0.01539245,0.001343981,0.00478034,0.002471396,0.0005633619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002697584,0.000340056,0.008212704,0.003679239,0.0009246498,0.00207257,0.003412835,0.05289418,0.00259275,0.451772,0.02502171,0.4488076],"study_design_scores_gemma":[0.00005180578,0.00005345629,0.0009790881,0.0005878833,0.0001704828,0.0008758942,0.001402628,0.160423,0.003199575,0.7658007,0.06637292,0.00008244615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01563673,0.01194994,0.9507564,0.005821237,0.0002772351,0.0006071198,0.003406681,0.00227658,0.009268159],"genre_scores_gemma":[0.1038451,0.009233894,0.876213,0.001321692,0.0002552347,0.000458564,0.005692796,0.0001762886,0.00280346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01416133,"threshold_uncertainty_score":0.06721574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06937896728329003,"score_gpt":0.3180131510689414,"score_spread":0.2486341837856514,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}